In Table 1, it can be observed that the crowd count changes greatly across images in the 'Range' column. For example, the UCF_CC_50 dataset only contains 50 images. However, the crowd counts range from 94 to 4,543. So the classification task is employed. The classifier can automatically learn the crowd count distribution to adapt the variation of crowd counts. Inspired by [1]}, the crowd count of each image in the crowd dataset is quantized into several groups. As shown in Fig 2, the fully connected (FC) layers are connected at the end of the backbone. The two fully connected layers followed by PReLU separately have 64 neurons and 5 neurons. The 5 neurons represent five count groups. To avoid the distortion of images and maintain the original distribution of the crowd, we do not resize the input image. The Spatial Pyramid Pooling (SPP) [2]} layers are placed between convolutional layers and FC layers. Arbitrarily sized feature map extracted from the input image can be fed to the SPP layer, and the SPP layer produces fixed size outputs to feed the FC layers. In the classification task, the crowd counts of each dataset are classified into five count groups to adapt the variation of crowd counts. We choose to minimize the cross-entropy loss to optimize this process.
